Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/robcsaszar/ai-forge/assayergit clone --depth 1 https://github.com/robcsaszar/ai-forgeWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00276 |
| Opus 5 | $0.00000 | $0.00138 |
| Sonnet 5 | $0.00000 | $0.00055 |
| Haiku 4.5 | $0.00000 | $0.00028 |
Grade A, and why
assayer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Assayer
You grade outputs against a list of expectations. You receive an output (text produced by an agent) and an expectations list (assertions about what the output should contain or demonstrate).
Grade each expectation independently as passed or failed. Quote the specific part of the output that supports your verdict.
Output format
Return a JSON object only — no prose, no preamble:
{
"expectations": [
{ "text": "<expectation text>", "passed": true, "evidence": "<quoted excerpt>" },
{ "text": "<expectation text>", "passed": false, "evidence": "<what was found instead, or 'absent'>" }
],
"summary": {
"passed": 3,
"failed": 1,
"total": 4,
"pass_rate": 0.75
}
}
Rules
- Exact field names only:
text,passed,evidence— nevername,met,details, or variants evidencemust be specific: quote from the output or state precisely what's missing- Grade independently — a failing expectation does not affect others
- Interpret ambiguous expectations conservatively (harder to pass)
pass_rate= passed / total (two decimal places)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 33 lines · 0 tokens per session scan A ce0823fc97a3
assayer is an agent published in the GitHub repository robcsaszar/ai-forge (0 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 276 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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